Why Cloud Native 2.0 is a Necessity for Agentic Enterprises

Enterprise cloud adoption in its first 10 years relied on lift and shift strategies. Legacy workloads moved to virtualised environments to cut hardware costs.

A different model has emerged today. Cloud Native 2.0 focuses on how applications are architected to use distributed infrastructure rather than where they are hosted.

Cloud-native design is essential for scaling Gen AI and autonomous operations, which cannot scale effectively on monolithic architectures.

Legacy systems lack the elasticity to handle fluctuating compute demands from large language models and autonomous agents. Cloud-native principles offer granular control for deploying AI at the edge and in the core simultaneously.

This modularity allows organisations to treat infrastructure as a dynamic fabric rather than a static foundation.

Microsoft's cloud-first transformation

Microsoft has restructured its enterprise identity through a cloud-first and AI-first approach. The company shifted its internal productivity suite and third-party ecosystem to a cloud-native Azure foundation.

This architecture enabled the rollout of Microsoft 365 Copilot to millions of users. The system uses a microservices mesh that allows rapid deployment of AI plug-ins across its tech stack.

Every layer of the enterprise becomes programmable and extensible through this model. Satya Nadella, Chairman and Chief Executive Officer of Microsoft, describes the shift as an economic necessity.

"Cloud-native applications are 10 to 100 times better in many cases," Satya says. Microsoft's approach prioritises data liquidity to ensure information flows securely between cloud-native applications.

This flow feeds the requirements of generative models. The architecture has allowed Microsoft to maintain a leadership position by providing infrastructure for other companies to build digital operations.

Microsoft states this architectural model acts as a way to hedge against demand cycles. "By moving to the cloud, you only consume when you need it," Satya says.

The modularity ensures that as AI models evolve, the underlying infrastructure remains resilient and capable of handling exponential increases in compute demand.

Huawei's AI-native infrastructure

Huawei has moved from telecommunications hardware to AI-native cloud infrastructure. The company has focused on its All Intelligence strategy, building a sovereign computing backbone for large enterprises.

Huawei's architecture is built on the CloudMatrix foundation. This high-performance distributed system pools CPUs, NPUs and DPUs to create a fabric for AI workloads.

Jacqueline Shi, President of Huawei Cloud Global Marketing and Sales Service, says the company is at a turning point. "By transitioning from cloud-native to AI-native, Huawei Cloud leads the industry with non-stop innovation," Jacqueline says.

"AI is our core strategy, building an optimal platform for accelerating development." 

Huawei integrates its Pangu large models directly into the cloud stack. This integration enables sectors like finance, government and manufacturing to deploy autonomous agentic AI systems.

The hybrid cloud platform Huawei Cloud Foundation remains important for large enterprises that require public cloud agility while maintaining data sovereignty and security within their borders.

This dual focus on infrastructure resilience and vertical-specific AI applications positions Huawei as an architect for intelligent enterprise operations.

Google's open cloud approach

Alphabet is championing the Open Cloud movement through Google Cloud. The company created and open-sourced Kubernetes, making it a major influence in the industry.

Google provides an environment optimised for deep learning and data-heavy workloads. Partnerships with automotive companies like Mercedes-Benz and BMW showcase this capability.

Vertex AI and Kubernetes Engine are used by these companies to build digital twins and AI agents.

These systems can be used for anything from optimising factory floor simulations to powering the natural-language MBUX Virtual Assistant.

Google's stack is built for data liquidity, ensuring real-time learning algorithms can enhance decision-making across multi-cloud environments.

Thomas Kurian, CEO of Google Cloud, views this as the defining shift of the decade.

"We are in an entirely new era of cloud, fuelled by Gen AI," he says. "Our focus is on putting Gen AI tools into the hands of everyone across the organisation, from IT to operations, to security and to the boardroom.

"As the industry's most open cloud, our goal is to help companies use AI and other cloud technologies to streamline their operations, increase productivity and create entirely new lines of business."

By focusing on open cloud principles and advanced grounding techniques, which link AI models to verifiable enterprise data, Google is ensuring clients are not locked into a single vendor.